Molecular Characteristics and Metastasis Predictor Genes of Triple-Negative Breast Cancer: A Clinical Study of Triple-Negative Breast Carcinomas

Background

Triple-negative breast cancer is a subtype of breast cancer with aggressive tumor behavior and distinct disease etiology. Due to the lack of an effective targeted medicine, treatment options for triple-negative breast cancer are few and recurrence rates are high. Although various multi-gene prognostic markers have been proposed for the prediction of breast cancer outcome, most of them were proven clinically useful only for estrogen receptor-positive breast cancers. Reliable identification of triple-negative patients with a favorable prognosis is not yet possible.

Methodology/Principal Findings

Clinicopathological information and microarray data from 157 invasive breast carcinomas were collected at National Taiwan University Hospital from 1995 to 2008. Gene expression data of 51 triple-negative and 106 luminal breast cancers were generated by oligonucleotide microarrays. Hierarchical clustering analysis revealed that the majority (94%) of triple-negative breast cancers were tightly clustered together carrying strong basal-like characteristics. A 45-gene prognostic signature giving 98% predictive accuracy in distant recurrence of our triple-negative patients was determined using the receiver operating characteristic analysis and leave-one-out cross validation. External validation of the prognostic signature in an independent microarray dataset of 59 early-stage triple-negative patients also obtained statistical significance (hazard ratio 2.29, 95% confidence interval (CI) 1.04–5.06, Cox P = 0.04), outperforming five other published breast cancer prognostic signatures. The 45-gene signature identified in this study revealed that TGF-β signaling of immune/inflammatory regulation may play an important role in distant metastatic invasion of triple-negative breast cancer.

Conclusions/Significance

Gene expression data and recurrence information of triple-negative breast cancer were collected and analyzed in this study. A novel set of 45-gene signature was found to be statistically predictive in disease recurrence of triple-negative breast cancer. The 45-gene signature, if further validated, may be a clinically useful tool in risk assessment of distant recurrence for early-stage triple-negative patients.     

Transcriptional landscape of DNA repair genes underpins a pan-cancer prognostic signature associated with cell cycle dysregulation and tumor hypoxia

Overactive DNA repair contributes to therapeutic resistance in cancer. However, pan-cancer comparative studies investigating the contribution of all DNA repair genes in cancer progression employing an integrated approach have remained limited. We performed a multi-cohort retrospective analysis to determine the prognostic significance of 138 DNA repair genes in 16 cancer types (n = 16,225). Cox proportional hazards analyses revealed a significant variation in the number of prognostic genes between cancers; 81 genes were prognostic in clear cell renal cell carcinoma while only two genes were prognostic in glioblastoma. We reasoned that genes that were commonly prognostic in highly correlated cancers revealed by Spearman’s correlation analysis could be harnessed as a molecular signature for risk assessment. A 10-gene signature, uniting prognostic genes that were common in highly correlated cancers, was significantly associated with overall survival in patients with clear cell renal cell (P < 0.0001), papillary renal cell (P = 0.0007), liver (P = 0.002), lung (P = 0.028), pancreas (P = 0.00013) or endometrial (P = 0.00063) cancers. Receiver operating characteristic analyses revealed that a combined model of the 10-gene signature and tumor staging outperformed either classifier when considered alone. Multivariate Cox regression models incorporating additional clinicopathological features showed that the signature was an independent predictor of overall survival. Tumor hypoxia is associated with adverse outcomes. Consistent across all six cancers, patients with high 10-gene and high hypoxia scores had significantly higher mortality rates compared to those with low 10-gene and low hypoxia scores. Functional enrichment analyses revealed that high mortality rates in patients with high 10-gene scores were attributable to an overproliferation phenotype. Death risk in these patients was further exacerbated by concurrent mutations of a cell cycle checkpoint protein, TP53. The 10-gene signature identified tumors with heightened DNA repair ability. This information has the potential to radically change prognosis through the use of adjuvant DNA repair inhibitors with chemotherapeutic drugs.     

A network module-based method for identifying cancer prognostic signatures

Discovering robust prognostic gene signatures as biomarkers using genomics data can be challenging. We have developed a simple but efficient method for discovering prognostic biomarkers in cancer gene expression data sets using modules derived from a highly reliable gene functional interaction network. When applied to breast cancer, we discover a novel 31-gene signature associated with patient survival. The signature replicates across 5 independent gene expression studies, and outperforms 48 published gene signatures. When applied to ovarian cancer, the algorithm identifies a 75-gene signature associated with patient survival. A Cytoscape plugin implementation of the signature discovery method is available at http://wiki.reactome.org/index.php/Reactome_FI_Cytoscape_Plugin     

Tumor markers in breast cancer--evaluation of their clinical usefulness

Breast cancer is the most common neoplasm affecting women in the Western world. Many studies are still conducted with the purpose of finding markers that could be used for early diagnosis and/or serve as possible reliable prognostic or predictive parameters, but with conflicting results. At present, no markers are available for an early diagnosis of breast cancer For surveillance of patients with diagnosed breast cancer the most widely used serum markers are CA 15-3 and CEA which, in combination with other clinical parameters, could have clinical significance. The most useful and clinically important tissue-based markers in breast cancer are estrogen and progesterone receptors, used as a basis for hormonal therapy, and HER-2 receptors, essential in selecting patients for the treatment with Herceptin. New or potentially new markers for breast cancer include BRCA1 and BRCA2 genes for selecting patients at high risk of developing hereditary breast cancer, as well as urokinase plasminogen activator and inhibitor for assessing prognosis in lymph node-negative patients. Results of tumor and patient genetic analyses including their clinical evaluation will enable application of more individualized and personalized approach in diagnosis and therapy of breast cancer patients.     

Homogeneous datasets of triple negative breast cancers enable the identification of novel prognostic and predictive signatures

Background

Current prognostic gene signatures for breast cancer mainly reflect proliferation status and have limited value in triple-negative (TNBC) cancers. The identification of prognostic signatures from TNBC cohorts was limited in the past due to small sample sizes.

Methodology/Principal Findings

We assembled all currently publically available TNBC gene expression datasets generated on Affymetrix gene chips. Inter-laboratory variation was minimized by filtering methods for both samples and genes. Supervised analysis was performed to identify prognostic signatures from 394 cases which were subsequently tested on an independent validation cohort (n = 261 cases).

Conclusions/Significance

Using two distinct false discovery rate thresholds, 25% and <3.5%, a larger (n = 264 probesets) and a smaller (n = 26 probesets) prognostic gene sets were identified and used as prognostic predictors. Most of these genes were positively associated with poor prognosis and correlated to metagenes for inflammation and angiogenesis. No correlation to other previously published prognostic signatures (recurrence score, genomic grade index, 70-gene signature, wound response signature, 7-gene immune response module, stroma derived prognostic predictor, and a medullary like signature) was observed. In multivariate analyses in the validation cohort the two signatures showed hazard ratios of 4.03 (95% confidence interval [CI] 1.71–9.48; P = 0.001) and 4.08 (95% CI 1.79–9.28; P = 0.001), respectively. The 10-year event-free survival was 70% for the good risk and 20% for the high risk group. The 26-gene signatures had modest predictive value (AUC = 0.588) to predict response to neoadjuvant chemotherapy, however, the combination of a B-cell metagene with the prognostic signatures increased its response predictive value. We identified a 264-gene prognostic signature for TNBC which is unrelated to previously known prognostic signatures.     

The CIN4 Chromosomal Instability qPCR Classifier Defines Tumor Aneuploidy and Stratifies Outcome in Grade 2 Breast Cancer

Purpose

Quantifying chromosomal instability (CIN) has both prognostic and predictive clinical utility in breast cancer. In order to establish a robust and clinically applicable gene expression-based measure of CIN, we assessed the ability of four qPCR quantified genes selected from the 70-gene Chromosomal Instability (CIN70) expression signature to stratify outcome in patients with grade 2 breast cancer.

Methods

AURKA, FOXM1, TOP2A and TPX2 (CIN4), were selected from the CIN70 signature due to their high level of correlation with histological grade and mean CIN70 signature expression in silico. We assessed the ability of CIN4 to stratify outcome in an independent cohort of patients diagnosed between 1999 and 2002. 185 formalin-fixed, paraffin-embedded (FFPE) samples were included in the qPCR measurement of CIN4 expression. In parallel, ploidy status of tumors was assessed by flow cytometry. We investigated whether the categorical CIN4 score derived from the CIN4 signature was correlated with recurrence-free survival (RFS) and ploidy status in this cohort.

Results

We observed a significant association of tumor proliferation, defined by Ki67 and mitotic index (MI), with both CIN4 expression and aneuploidy. The CIN4 score stratified grade 2 carcinomas into good and poor prognostic cohorts (mean RFS: 83.8±4.9 and 69.4±8.2 months, respectively, p = 0.016) and its predictive power was confirmed by multivariate analysis outperforming MI and Ki67 expression.

Conclusions

The first clinically applicable qPCR derived measure of tumor aneuploidy from FFPE tissue, stratifies grade 2 tumors into good and poor prognosis groups.     

Using Functional Genetics to Understand Breast Cancer Biology

Genetic screens were for long the prerogative of those that studied model organisms. The discovery in 2001 that gene silencing through RNA interference (RNAi) can also be brought about in mammalian cells paved the way for large scale loss-of-function genetic screens in higher organisms. In this article, we describe how functional genetic studies can help us understand the biology of breast cancer, how it can be used to identify novel targets for breast cancer therapy, and how it can help in the identification of those patients that are most likely to respond to a given therapy.Much remains to be learned regarding the function of mammalian genes. Only some quarter of all human genes have well-described functions. It is likely that quite a few of these currently unannotated genes will turn out to play key parts in cancer biology. For example, a 70-gene gene signature that can discriminate breast tumors of good and poor prognosis contained some 20 genes of currently unknown function (van ‘t Veer et al. 2002). The fact that these genes of unknown function foretell breast cancer prognosis hints at a role for at least some of these genes in breast cancer biology. The unbiased search for genes that contribute to breast cancer development is therefore likely to yield a rich harvest of new insights. RNA interference allows us to suppress genes systematically on a large scale and study the effects of gene suppression on specific cellular processes or signaling pathways. Consequently, RNA interference-based genetic screens have the potential to deepen our understanding of the molecular events that cause breast cancer, to find novel targets for therapy and to find biomarkers of drug responsiveness. In this article, we will describe the technologies available to perform both gain-of-function and loss-of-function genetic screens and will illustrate how such functional genetic screens have been used in the recent past to study a variety of outstanding questions in the biology of breast cancer.     

A prognostic gene expression profile that predicts circulating tumor cell presence in breast cancer patients

The detection of circulating tumor cells (CTCs) in the peripheral blood and microarray gene expression profiling of the primary tumor are two promising new technologies able to provide valuable prognostic data for patients with breast cancer. Meta-analyses of several established prognostic breast cancer gene expression profiles in large patient cohorts have demonstrated that despite sharing few genes, their delineation of patients into "good prognosis" or "poor prognosis" are frequently very highly correlated, and combining prognostic profiles does not increase prognostic power. In the current study, we aimed to develop a novel profile which provided independent prognostic data by building a signature predictive of CTC status rather than outcome. Microarray gene expression data from an initial training cohort of 72 breast cancer patients for which CTC status had been determined in a previous study using a multimarker QPCR-based assay was used to develop a CTC-predictive profile. The generated profile was validated in two independent datasets of 49 and 123 patients and confirmed to be both predictive of CTC status, and independently prognostic. Importantly, the "CTC profile" also provided prognostic information independent of the well-established and powerful '70-gene' prognostic breast cancer signature. This profile therefore has the potential to not only add prognostic information to currently-available microarray tests but in some circumstances even replace blood-based prognostic CTC tests at time of diagnosis for those patients already undergoing testing by multigene assays.     

Gene Signatures in Breast Cancer: Current and Future Uses

Gene signatures have been developed for estrogen receptor-positive breast cancer to complement pathological factors in providing prognostic information. The 70-gene and the 21-gene signatures identify patients who may not require adjuvant chemotherapy. Gene signatures in triple-negative disease and HER2-positive disease have not been fully developed yet, although studies demonstrate heterogeneity within these subgroups. Further research is needed before genotyping will help in making clinical decisions in triple-negative and HER2-positive disease. Molecular subtyping of breast cancer led to define luminal, basal, and HER2-enriched subtypes, which have specific clinical behavior. This approach may lead to identify new subgroups requiring specific therapies. Standardization of techniques will be required to translate investigations to the clinic.     

Inference of patient‐specific subpathway activities reveals a functional signature associated with the prognosis of patients with breast cancer

Breast cancer is one of the most deadly forms of cancer in women worldwide. Better prediction of breast cancer prognosis is essential for more personalized treatment. In this study, we aimed to infer patient‐specific subpathway activities to reveal a functional signature associated with the prognosis of patients with breast cancer. We integrated pathway structure with gene expression data to construct patient‐specific subpathway activity profiles using a greedy search algorithm. A four‐subpathway prognostic signature was developed in the training set using a random forest supervised classification algorithm and a prognostic score model with the activity profiles. According to the signature, patients were classified into high‐risk and low‐risk groups with significantly different overall survival in the training set (median survival of 65 vs 106 months, P = 1.82e‐13) and test set (median survival of 75 vs 101 months, P = 4.17e‐5). Our signature was then applied to five independent breast cancer data sets and showed similar prognostic values, confirming the accuracy and robustness of the subpathway signature. Stratified analysis suggested that the four‐subpathway signature had prognostic value within subtypes of breast cancer. Our results suggest that the four‐subpathway signature may be a useful biomarker for breast cancer prognosis.     

Circulating tumour cells lacking cytokeratin in breast cancer: the importance of being mesenchymal

Circulating tumour cells (CTCs) are independent predictor of prognosis in metastatic breast cancer. Nevertheless, in one third of patients, circulating tumour cells are undetected by conventional methods. Aim of the study was to assess the prognostic value of circulating tumour cells expressing mesenchymal markers in metastatic breast cancer patients. We isolated CTC from blood of 55 metastatic breast cancer patients. CTC were characterized for cytokeratins and markers of epithelial mesenchymal transition. The gain of mesenchymal markers in CTC was correlated to prognosis of patients in a follow-up of 24 months. The presence of mesenchymal markers on CTC more accurately predicted worse prognosis than the expression of cytokeratins alone. Because of the frequent loss of epithelial antigens by CTC, assays targeting epithelial antigens may miss the most invasive cell population. Thus, there is an urgent need to improve detection methods to identify CTC which undergone epithelial mesenchymal transition program.     

An estrogen receptor (ER)‐related signature in predicting prognosis of ER‐positive breast cancer following endocrine treatment

Quite a few estrogen receptor (ER)‐positive breast cancer patients receiving endocrine therapy are at risk of disease recurrence and death. ER‐related genes are involved in the progression and chemoresistance of breast cancer. In this study, we identified an ER‐related gene signature that can predict the prognosis of ER‐positive breast cancer patient receiving endocrine therapy. We collected RNA expression profiling from Gene Expression Omnibus database. An ER‐related signature was developed to separate patients into high‐risk and low‐risk groups. Patients in the low‐risk group had significantly better survival than those in the high‐risk group. ROC analysis indicated that this signature exhibited good diagnostic efficiency for the 1‐, 3‐ and 5‐year disease‐relapse events. Moreover, multivariate Cox regression analysis demonstrated that the ER‐related signature was an independent risk factor when adjusting for several clinical signatures. The prognostic value of this signature was validated in the validation sets. In addition, a nomogram was built and the calibration plots analysis indicated the good performance of this nomogram. In conclusion, combining with ER status, our results demonstrated that the ER‐related prognostic signature is a promising method for predicting the prognosis of ER‐positive breast cancer patients receiving endocrine therapy.     

RB-pathway disruption in breast cancer: Differential association with disease subtypes,disease-specific prognosis and therapeutic response

In breast cancer, inactivation of the RB tumor suppressor gene is believed to occur via multiple mechanisms to facilitate tumorigenesis. However, the prognostic and predictive value of RB status in disease-specific clinical outcomes has remained uncertain. We investigated RB pathway deregulation in the context of both ER-positive and ER-negative disease using combined microarray datasets encompassing over 900 breast cancer patient samples. Disease-specific characteristics of RB pathway deregulation were investigated in this dataset by evaluating correlation among pathway genes as well as differential expression across patient tumor populations defined by ER status. Survival analysis among these breast cancer samples demonstrates that the RB-loss signature is associated with poor disease outcome within several independent cohorts. Within the ER-negative subpopulation, the RB-loss signature is associated with improved response to chemotherapy and longer relapse-free survival. Additionally, while individual genes in the RB target signature closely reproduce its prognostic value, they also serve to predict and monitor response to therapeutic compounds, such as the cytostatic agent PD-0332991. These results indicate that the RB-loss signature expression is associated with poor outcome in breast cancer, but predicts improved response to chemotherapy based on data in ER-negative populations. While the RB-loss signature, as a whole, demonstrates prognostic and predictive utility, a small subset of markers could be sufficient to stratify patients based on RB function and inform the selection of appropriate therapeutic regimens.Key words: RB, breast cancer, microarray, proliferation, cytostatics     

Integrated analysis of gene expression and DNA methylation datasets identified key genes and a 6-gene prognostic signature for primary lung adenocarcinoma

Lung adenocarcinoma (LUAD) is the main subtype of non-small cell lung cancer with a poor survival prognosis. In our study, gene expression, DNA methylation, and clinicopathological data of primary LUAD were utilized to identify potential prognostic markers for LUAD, which were recruited from The Cancer Genome Atlas (TCGA) database. Univariate regression analysis showed that there were 21 methylation-associated DEGs related to overall survival (OS), including 9 down- and 12 up-regulated genes. The 12 up-regulated genes with hypomethylation may be risky genes, whereas the other 9 down-regulated genes with hypermethylation might be protective genes. By using the Step-wise multivariate Cox analysis, a methylation-associated 6-gene (consisting of CCL20, F2, GNPNAT1, NT5E, B3GALT2, and VSIG2) prognostic signature was constructed and the risk score based on this gene signature classified patients into high- or low-risk groups. Patients of the high-risk group had shorter OS than those of the low-risk group in both the training and validation cohort. Multivariate Cox analysis and the stratified analysis revealed that the risk score was an independent prognostic factor for LUAD patients. The methylation-associated gene signature may serve as a prognostic factor for LUAD patients and the represent hypermethylated or hypomethylated genes might be potential targets for LUAD therapy.     

Identification of Gene-Expression Signatures and Protein Markers for Breast Cancer Grading and Staging

The grade of a cancer is a measure of the cancer''s malignancy level, and the stage of a cancer refers to the size and the extent that the cancer has spread. Here we present a computational method for prediction of gene signatures and blood/urine protein markers for breast cancer grades and stages based on RNA-seq data, which are retrieved from the TCGA breast cancer dataset and cover 111 pairs of disease and matching adjacent noncancerous tissues with pathologists-assigned stages and grades. By applying a differential expression and an SVM-based classification approach, we found that 324 and 227 genes in cancer have their expression levels consistently up-regulated vs. their matching controls in a grade- and stage-dependent manner, respectively. By using these genes, we predicted a 9-gene panel as a gene signature for distinguishing poorly differentiated from moderately and well differentiated breast cancers, and a 19-gene panel as a gene signature for discriminating between the moderately and well differentiated breast cancers. Similarly, a 30-gene panel and a 21-gene panel are predicted as gene signatures for distinguishing advanced stage (stages III-IV) from early stage (stages I-II) cancer samples and for distinguishing stage II from stage I samples, respectively. We expect these gene panels can be used as gene-expression signatures for cancer grade and stage classification. In addition, of the 324 grade-dependent genes, 188 and 66 encode proteins that are predicted to be blood-secretory and urine-excretory, respectively; and of the 227 stage-dependent genes, 123 and 51 encode proteins predicted to be blood-secretory and urine-excretory, respectively. We anticipate that some combinations of these blood and urine proteins could serve as markers for monitoring breast cancer at specific grades and stages through blood and urine tests.     

Good practice guidelines for biomarker discovery from array data: a case study for breast cancer prognosis

Background

Biomarker discovery holds the promise for advancing personalized medicine as the biomarkers can help match patients to optimal treatment to improve patient outcomes. However, serious concerns have been raised because very few molecular biomarkers or signatures discovered from high dimensional array data can be successfully validated and applied to clinical use. We propose good practice guidelines as well as a novel tool for biomarker discovery and use breast cancer prognosis as a case study to illustrate the proposed approach.

Results

We applied the proposed approach to a publicly available breast cancer prognosis dataset and identified small numbers of predictive markers for patient subpopulations stratified by clinical variables. Results from an independent cross-platform validation set show that our model compares favorably to other gene signature and clinical variable based prognostic tools. About half of the discovered candidate markers can individually achieve very good performance, which further demonstrate the high quality of feature selection. These candidate markers perform extremely well for young patient with estrogen receptor-positive, lymph node-negative early stage breast cancers, suggesting a distinct subset of these patients identified by these markers is actually at high risk of recurrence and may benefit from more aggressive treatment than cur-rent practice.

Conclusion

The results show that by following good practice guidelines, we can identify highly predictive genes in high dimensional breast cancer array data. These predictive genes have been successfully validated using an independent cross-platform dataset.